Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling focus of attention for meeting indexing
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-linear dimensionality reduction techniques for classification and visualization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An Algorithm for Real-Time Stereo Vision Implementation of Head Pose and Gaze Direction Measurement
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Image Spaces and Video Trajectories: Using Isomap to Explore Video Sequences
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Identifying Individuals in Video by Combining "Generative" and Discriminative Head Models
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Recognition of human head orientation based on artificial neural networks
IEEE Transactions on Neural Networks
Locating nose-tips and estimating head poses in images by tensorposes
IEEE Transactions on Circuits and Systems for Video Technology
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We present a new scheme to robustly detect a type of human attentive behavior, which we call frequent change in focus of attention (FCFA), from video sequences. FCFA behavior can be easily perceived by people as temporal changes of human head pose (normally the pan angle). For recognition of this behavior by computer, we propose an algorithm to estimate the head pan angle in each frame of the sequence within a normal range of the head tilt angles. Developed from the ISOMAP, we learn a non-linear head pose embedding space in 2-D, which is suitable as a feature space for person-independent head pose estimation. These features are used in a mapping system to map the high dimensional head images into the 2-D feature space from which the head pan angle is calculated very simply. The non-linear person-independent mapping system is composed of two parts: 1) Radial Basis Function (RBF) interpolation, and 2) an adaptive local fitting technique. The results show that head orientation can be estimated robustly. Following the head pan angle estimation, an entropy-based classifier is used to characterize the attentive behaviors. The experimental results show that entropy of the head pan angle is a good measure, which is quite distinct for FCFA and focused attention behavior. Thus by setting an experimental threshold on the entropy value we can successfully and robustly detect FCFA behavior.